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A Concise Review of Recent Few-shot Meta-learning Methods

Li, X; Sun, Z; Xue, J; Ma, Z; (2021) A Concise Review of Recent Few-shot Meta-learning Methods. Neurocomputing 10.1016/j.neucom.2020.05.114. (In press).

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Abstract

Few-shot meta-learning has been recently reviving with expectations to mimic humanity’s fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning.

Type: Article
Title: A Concise Review of Recent Few-shot Meta-learning Methods
DOI: 10.1016/j.neucom.2020.05.114
Publisher version: https://doi.org/10.1016/j.neucom.2020.05.114
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Meta Learning, Few-shot Learning, Image Classification, Deep Neural Networks, Small-sample Learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10103880
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